Irene Helenowski's Blog, page 9

A Weighty Issue

So why don’t we talk about another way to handle missing data which involves weighted estimating equations, or WEE.  Isn’t it fun just to say at least?  WEE!!!!  And what it is is it models entries with different weights, depending on how much missing data is in that entry.  Kinda like how Randy would assign different weights to dimensions where he could find Anton depending on how much info he had or didn’t have on that dimension.  And how can he do that? Well … we can get into that next week.  Until then …

WEEEEEEEEEE!!!!!!!!!!!! …………………..

Getting skewed with skewness

Hello! Did everyone have a good New Year?  Great!  So here we go — today, I’ll talk about skewness which basically tells you which values are more likely to happen based on whether they are higher or lower than the median value.  So if Randy were more likely to find Anton in a warmer climate, he’d want to look at a distribution of dimensions skewed towards higher temperatures if in fact the distribution he was looking at a distribution of temperatures of the dimensions. Kinda like this …

 

 

 

So there you have it.  Skewness in a nutshell.  Well, until next time!!  And if you need some reading material then … yeah, I’m stopping.

New Posts coming in The New Year

So stay tuned!!

 

Happy Holidays!!

And see next week or two weeks which would be the next year!!  And remember this for a last minute gift idea!!  Till then, toodles …

 

The Mode

Okay … lets talk about … the mode!  And what is … the mode?  Well … the mode … is the most frequent number in a list of numbers.  So lets say Randy is given a list of dimensions where Anton is.  And some dimensions appear more than one and more than some others on the list.  The dimensions that appears the most on the list would be … the mode!!  Now, wasn’t that an easy lesson for today?  Yeah, thought so too ;)  So anywhere what dimension appears the most with Anton in it anyway?  Well, you know how to figure that out!

But anywhoo, till next week!

And now a poem about … the tetrachoric correlation?

You have two underlying variables,

Following a distribution so normal,

But then you see that they are,

Binary after all.

 

Sort of like Randy finding Anton,

In a world good or bad,

Maybe underlying bad Anton,

Is the weather of income that is said.

 

So another poem is created,

Maybe next time we do one with the fantasy cast or no?

We will yet see,

But the perfect holiday gift for a scifi lover is, you know.

 

Fun with the CV

So the coefficient of variance or CV came up quite a bit at work recently and so I decided to talk about that.  Why? Because I can!  Because it’s my blog!  And because it’s another short and sweet topic that we can cover before you go on with your holiday shopping ** cough, cough ** Order of The Dimensions ** cough, cough ** perfect gift for any holiday lover ** cough, cough **.  And what it is is a measure of the variation relative to the mean.  So ideally, we for the CV to be lower but a low standard deviation and a low mean could give a similar CV  as a high standard deviation and a high mean so really we want a low standard deviation and a high mean.  Got all that?  Good, I hope.  But just to be sure I’m doing another Randy-Anton example.  So say Randy’s looking at three dimensions, one where he has a high probability of catching Anton among a lot of places but those places are so far apart, one where he has a low probability of catching Anton among a lot of places but those places are close together, and  one where he has a high probability of catching Anton among a lot of places and those places are close together.  So where would he look first?  There you go — the third one as that would be his “low CV” as you will.  But does he find such a dimension?  Well, maybe you and the scifi lover on your holiday list then … yeah ;)

Back Next Week!

Until then, here’s a picture of my favorite Disney character, Baloo from Talespin. Who’s a seaplane pilot.  Just like Randy is a seaplane pilot in some dimensions.  Yes, in some dimensions where … oh, you know

 

Poem about … random slopes?

Why not? Here goes.

 

One can go up.

One can go down.

One can make you smile.

One can make you frown.

 

Some are known as BLUPS.

And they could lead Randy to Anton’s alters.

Whether he’s at the Marriot on the hill.

Or the Sheraton by the waters.

 

And why did mention such hotels?

Or the Hilton or the Hyatt?

Just have some exciting news.

But to keep silent now is my best bet :D

 

But my so many hotel mentions in this week’s poem?   Anyway, more on that later — if it works out ;)  Until then, have a good weekend, peeps!

 

AUC poem

It may tell us if what we predict is right.

Like where Anton may spend the night.

This is what Randy needs to know.

So the area-under-the-curve (AUC) can’t be too low.

 

Can you just handle all the suspense?

And will you then give my trilogy a chance?

If so, I do hope you give it more than 50 percent.

If I do say so myself, it would be money well spent.